Databricks Machine Learning Associate Question 555
Select 4You are training a classification model on a highly imbalanced dataset where the minority class constitutes only 5% of the data. Which of the following methods can help mitigate the impact of this imbalance and improve model performance?
- A
Oversample the minority class using techniques such as SMOTE (Synthetic Minority Oversampling Technique).
- B
Adjust the class weights during model training to penalize misclassifications of the minority class more heavily.
- C
Remove instances from the majority class to balance the dataset.
- D
Use a different optimizer to minimize the loss function during training.
- E
Use performance metrics such as Precision, Recall, or F1 Score to better evaluate the model.
Show answer and explanation
Correct answers: A, B, C, E
Explanation
Dealing with data imbalance is critical for building effective models in scenarios with skewed class distributions. Methods such as oversampling the minority class, adjusting class weights, and undersampling the majority class directly address the imbalance in the dataset. Additionally, using appropriate performance metrics like Precision, Recall, or F1 Score helps evaluate the model's performance more reliably in imbalanced situations. However, simply changing the optimizer does not address the issue of class imbalance.
- A. Correct.
Oversampling the minority class using techniques like SMOTE is a common and effective method to address class imbalance by synthetically increasing the number of minority class samples.
- B. Correct.
Adjusting class weights during model training helps the model focus more on the minority class by penalizing its misclassification more heavily, making it an effective way to deal with imbalance.
- C. Correct.
Removing instances from the majority class (undersampling) can create a more balanced dataset, though it may risk losing valuable information from the majority class.
- D. Incorrect.
Changing the optimizer does not directly address class imbalance. Optimizers are related to how the model minimizes the loss function, not the underlying data distribution.
- E. Correct.
Using metrics like Precision, Recall, or F1 Score provides better insights into model performance on imbalanced datasets compared to accuracy, which can be misleading in such scenarios.